Back to Search
Start Over
A decision support system for predicting settling velocity of spherical and non-spherical particles in Newtonian fluids.
- Source :
- Particulate Science & Technology; 2022, Vol. 40 Issue 5, p609-619, 11p
- Publication Year :
- 2022
-
Abstract
- An artificial intelligence-based system was developed to efficiently predict settling velocity (SV) using a large dataset comprised of 2726 samples. The ranges of particle size and fluid viscosity were 0.212 − 98.59 mm and 0.02 − 92800 mPa.s, respectively. Properties of particle and fluid were fed to a model as the inputs to obtain SV as the output. Six machine learning algorithms were tested for the prediction. The random forest (RF) performed better than other algorithms with a coefficient of determination of 0.98 and a mean square error of 0.0027. A simple decision support system was developed using the RF model. The current study demonstrates the complete methodology of modeling SV with ML. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02726351
- Volume :
- 40
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Particulate Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 157383290
- Full Text :
- https://doi.org/10.1080/02726351.2021.1982092